Zobrazeno 1 - 10
of 12
pro vyhledávání: '"Rao Muhammad Umer"'
Publikováno v:
IEEE Access, Vol 11, Pp 16549-16556 (2023)
Federated Learning (FL) is a machine learning technique in which collaborative and distributed learning is performed, while the private data reside locally on the client. Rather than the data, only gradients are shared among all collaborative nodes w
Externí odkaz:
https://doaj.org/article/2194be699d55436bba561374ff47ec59
Publikováno v:
Machine Learning: Science and Technology, Vol 5, Iss 2, p 025041 (2024)
Federated learning (FL) is an evolving machine learning technique that allows collaborative model training without sharing the original data among participants. In real-world scenarios, data residing at multiple clients are often heterogeneous in ter
Externí odkaz:
https://doaj.org/article/7e363ca3202b42d0967e9ddef78f6beb
Publikováno v:
International Journal of Neural Systems.
Swarm Learning (SL) is a promising approach to perform the distributed and collaborative model training without any central server. However, data sensitivity is the main concern for privacy when collaborative training requires data sharing. A neural
Publikováno v:
6th International Conference on Smart and Sustainable Technologies
2021 6th International Conference on Smart and Sustainable Technologies (SpliTech)
2021 6th International Conference on Smart and Sustainable Technologies (SpliTech)
Recently, most of state-of-the-art single image super-resolution (SISR) methods have attained impressive performance by using deep convolutional neural networks (DCNNs). The existing SR methods have limited performance due to a fixed degradation sett
Publikováno v:
ICPR
Deep convolutional neural networks (CNNs) have recently achieved great success for single image super-resolution (SISR) task due to their powerful feature representation capabilities. The most recent deep learning based SISR methods focus on designin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2b6963a0163aa004b4cc4f5d7d0a38c9
http://arxiv.org/abs/2009.04809
http://arxiv.org/abs/2009.04809
Autor:
Shuangquan Wang, Ziluan Liu, Kanghyu Lee, Jie Cai, Ying Tai, Xueyi Zou, Junyang Chen, Haoning Wu, Haoyu Ren, Dongwoon Bai, Pablo Navarrete Michelini, Shuai Liu, Zibo Meng, Zhi-Song Liu, Kalpesh Prajapati, Wei Deng, Kyung-Ah Sohn, Christian Micheloni, Ziyao Zong, Yunhua Lu, Haijie Zhuo, Huibing Wang, Martin Danelljan, Hanseok Ko, Mostafa El-Khamy, Wan-Chi Siu, Chiu Man Ho, Biao Yang, Amin Kheradmand, Fuzhi Yang, Jaejun Yoo, Hao Li, Kaihua Cheng, Rao Muhammad Umer, Namhyuk Ahn, Yuanbo Zhou, Timothy Haoning Wu, Yun Cao, Xiaozhong Ji, Gwantae Kim, Se Young Chun, Andreas Lugmayr, Yong Hyeok Seo, Jungwon Lee, Radu Timofte, Tongtong Zhao
Publikováno v:
CVPR Workshops
This paper reviews the NTIRE 2020 challenge on real world super-resolution. It focuses on the participating methods and final results. The challenge addresses the real world setting, where paired true high and low-resolution images are unavailable. F
Autor:
Christian Micheloni, Rao Muhammad Umer
Publikováno v:
Computer Vision – ECCV 2020 Workshops ISBN: 9783030670696
ECCV Workshops (3)
ECCV Workshops (3)
Recent deep learning based single image super-resolution (SISR) methods mostly train their models in a clean data domain where the low-resolution (LR) and the high-resolution (HR) images come from noise-free settings (same domain) due to the bicubic
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7c1b4f9bb04ffaf551322719c78c38e1
https://doi.org/10.1007/978-3-030-67070-2_29
https://doi.org/10.1007/978-3-030-67070-2_29
Publikováno v:
CVPR Workshops
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Most current deep learning based single image super-resolution (SISR) methods focus on designing deeper / wider models to learn the non-linear mapping between low-resolution (LR) inputs and the high-resolution (HR) outputs from a large number of pair
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::129ea893b2577a85419398c92641b4b5
http://hdl.handle.net/11390/1190638
http://hdl.handle.net/11390/1190638
Autor:
Nan Nan, Stavros Tsogkas, Geun-Woo Jeon, Jun-Hyuk Kim, Xiaochuan Li, Jiande Jiang, Xiaotong Luo, Lei Zhang, Jun-Ho Choi, Vineeth Bhaskara, Yuzhi Zhao, Jong-Seok Lee, Shuai Liu, Maitreya Suin, Jian Cheng, Xiaohong Liu, Xuehui Wang, Jiangtao Nie, Wenyi Wang, Siang Chen, Martin Danelljan, Jiangtao Zhang, Rushi Lan, Yawei Li, Long Chen, Yu Zhu, Allan D. Jepson, Cong Leng, Christian Micheloni, Jie Cai, Shanshan Zhao, Chenghua Li, Rao Muhammad Umer, Guangyang Wu, C. V. Jiji, Zhenbing Liu, Zibo Meng, Eric Marty, Xinbo Gao, Qiong Yan, Wenhao Wang, Wei Wei, Subin Yang, A. N. Rajagopalan, Wen Lu, Radu Timofte, Jie Liu, Long Sun, Yu Qiao, Haicheng Wang, Yanyun Qu, Yongwoo Kim, Tongtong Zhao, Alex Levinshtein, Steven Marty, Wenjie Xu, JungHeum Kang, Xiangtao Kong, Xiangyu He, Densen Puthussery, Jie Tang, Jingwen He, Kai Zhang, Lin Zha, P. S. Hrishikesh, Hengyuan Zhao, Chao Dong, Sung-Ho Bae, Zhiqiang Lang, Abdul Muqeet, Xiangzhen Kong, Jiaming Ding, Dongliang Xiong, Chiu Man Ho, Jiwon Hwang, Gangshan Wu, Liang Chen, Kuldeep Purohit
Publikováno v:
Computer Vision – ECCV 2020 Workshops ISBN: 9783030670696
ECCV Workshops (3)
ECCV Workshops (3)
This paper reviews the AIM 2020 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The challenge task was to super-resolve an input image with a magnification factor \(\times \)4 based on a set of p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d21d570f8d10ec09dfbf7bc40c5c9c4a
https://doi.org/10.1007/978-3-030-67070-2_1
https://doi.org/10.1007/978-3-030-67070-2_1
Autor:
Pengxu Wei, Hannan Lu, Radu Timofte, Liang Lin, Wangmeng Zuo, Zhihong Pan, Baopu Li, Teng Xi, Yanwen Fan, Gang Zhang, Jingtuo Liu, Junyu Han, Errui Ding, Tangxin Xie, Liang Cao, Yan Zou, Yi Shen, Jialiang Zhang, Yu Jia, Kaihua Cheng, Chenhuan Wu, Yue Lin, Cen Liu, Yunbo Peng, Xueyi Zou, Zhipeng Luo, Yuehan Yao, Zhenyu Xu, Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Keon-Hee Ahn, Jun-Hyuk Kim, Jun-Ho Choi, Jong-Seok Lee, Tongtong Zhao, Shanshan Zhao, Yoseob Han, Byung-Hoon Kim, JaeHyun Baek, Haoning Wu, Dejia Xu, Bo Zhou, Wei Guan, Xiaobo Li, Chen Ye, Hao Li, Haoyu Zhong, Yukai Shi, Zhijing Yang, Xiaojun Yang, Xin Li, Xin Jin, Yaojun Wu, Yingxue Pang, Sen Liu, Zhi-Song Liu, Li-Wen Wang, Chu-Tak Li, Marie-Paule Cani, Wan-Chi Siu, Yuanbo Zhou, Rao Muhammad Umer, Christian Micheloni, Xiaofeng Cong, Rajat Gupta, Feras Almasri, Thomas Vandamme, Olivier Debeir
Publikováno v:
Computer Vision – ECCV 2020 Workshops ISBN: 9783030670696
ECCV Workshops (3)
ECCV Workshops (3)
This paper introduces the real image Super-Resolution (SR) challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ECCV 2020. This challenge involves three tracks to super-resolve an input image for \(\t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ef7216395fe2ce9733f5ad4d1ce38712
http://hdl.handle.net/11390/1206869
http://hdl.handle.net/11390/1206869